{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_7.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Aug 2020, 00:36:19
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,804,569       79.53       79.53
{txt}          2 {c |}{res}  1,330,268       10.79       90.32
{txt}          3 {c |}{res}    260,741        2.12       92.44
{txt}          4 {c |}{res}    250,433        2.03       94.47
{txt}          5 {c |}{res}    681,947        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,327,958      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,888,231       55.81       55.81
{txt}          2 {c |}{res}    924,747        7.49       63.31
{txt}          3 {c |}{res}  1,267,442       10.27       73.58
{txt}          4 {c |}{res}    237,647        1.93       75.50
{txt}          5 {c |}{res}  3,023,589       24.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,341,656      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,934,292       15.67       15.67
{txt}          2 {c |}{res}  3,555,999       28.81       44.48
{txt}          3 {c |}{res}  4,309,065       34.91       79.40
{txt}          4 {c |}{res}  2,543,032       20.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,342,388      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_individual i.Female i.AgeGrp4 i.Race5 i.BornUSA i.MarStat5 
{txt}
{com}. local demographics_county c.RAT_Female c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4  c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 c.RAT_BornUSA c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. 
. if (`model_version' == 7){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion' 
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 8){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion' 
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 9){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion'  i.UnEmpl i.PhysProb
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 UnEmpl PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 10){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion'  c.RAT_UnEmpl c.RAT_PhysProb
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 RAT_UnEmpl RAT_PhysProb
. {c )-}
{txt}
{com}. 
. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' >= 9){c -(}
.                 if (`model_version' != 10){c -(}
.                         estimates restore m1
.                         mchange_mi Female "binary" "mi"
.                         estimates restore m1
.                         mchange_mi AgeGrp4 "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi Race5 "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi BornUSA "binary" "mi"
.                         estimates restore m1
.                         mchange_mi MarStat5 "categorical" "mi"
.                 {c )-}
.                 
.                 if (`model_version' == 1 | `model_version' == 2 | `model_version' == 10) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4 | `model_version' == 9 ){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 10){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,815,159
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  418,785,985
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  42{txt},{res}    876{txt}){col 65}= {res}      684.51
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2} .8986798{col 26}{space 2} .0883434{col 37}{space 1}   -1.09{col 46}{space 3}0.277{col 54}{space 4}     .741{col 67}{space 3} 1.089913
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .6169936{col 26}{space 2} .0613791{col 37}{space 1}   -4.85{col 46}{space 3}0.000{col 54}{space 4} .5075636{col 67}{space 3} .7500166
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5548865{col 26}{space 2} .0564151{col 37}{space 1}   -5.79{col 46}{space 3}0.000{col 54}{space 4} .4545151{col 67}{space 3}  .677423
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5333315{col 26}{space 2} .0550444{col 37}{space 1}   -6.09{col 46}{space 3}0.000{col 54}{space 4} .4355419{col 67}{space 3} .6530774
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4061941{col 26}{space 2} .0404343{col 37}{space 1}   -9.05{col 46}{space 3}0.000{col 54}{space 4} .3341099{col 67}{space 3} .4938306
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4474546{col 26}{space 2} .0453585{col 37}{space 1}   -7.93{col 46}{space 3}0.000{col 54}{space 4} .3667318{col 67}{space 3} .5459457
{txt}{space 9}35  {c |}{col 14}{res}{space 2} 1.021002{col 26}{space 2} .1039374{col 37}{space 1}    0.20{col 46}{space 3}0.838{col 54}{space 4} .8361036{col 67}{space 3} 1.246789
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .6559779{col 26}{space 2} .0683746{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4} .5346242{col 67}{space 3} .8048776
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5474272{col 26}{space 2} .0593811{col 37}{space 1}   -5.55{col 46}{space 3}0.000{col 54}{space 4} .4424579{col 67}{space 3} .6772995
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .7534879{col 26}{space 2} .0752557{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4} .6193683{col 67}{space 3} .9166502
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4633785{col 26}{space 2} .0471454{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4} .3795052{col 67}{space 3} .5657883
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5855266{col 26}{space 2} .0596627{col 37}{space 1}   -5.25{col 46}{space 3}0.000{col 54}{space 4} .4794001{col 67}{space 3} .7151467
{txt}{space 9}49  {c |}{col 14}{res}{space 2} 1.885325{col 26}{space 2} .3620879{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} 1.293277{col 67}{space 3} 2.748404
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .7263621{col 26}{space 2}  .071488{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4} .5987808{col 67}{space 3}  .881127
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5614692{col 26}{space 2} .0565383{col 37}{space 1}   -5.73{col 46}{space 3}0.000{col 54}{space 4} .4607858{col 67}{space 3} .6841523
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2} .9388562{col 26}{space 2} .0168585{col 37}{space 1}   -3.51{col 46}{space 3}0.000{col 54}{space 4} .9063466{col 67}{space 3} .9725318
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.010992{col 26}{space 2} .0186812{col 37}{space 1}    0.59{col 46}{space 3}0.554{col 54}{space 4} .9749857{col 67}{space 3} 1.048327
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} 1.012114{col 26}{space 2} .0204798{col 37}{space 1}    0.60{col 46}{space 3}0.552{col 54}{space 4} .9727084{col 67}{space 3} 1.053115
{txt}{space 7}2009  {c |}{col 14}{res}{space 2} 1.042408{col 26}{space 2} .0208366{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} 1.002307{col 67}{space 3} 1.084113
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.035084{col 26}{space 2} .0216186{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4} .9935141{col 67}{space 3} 1.078393
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.079008{col 26}{space 2} .0235911{col 37}{space 1}    3.48{col 46}{space 3}0.001{col 54}{space 4} 1.033688{col 67}{space 3} 1.126314
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2} .2470189{col 26}{space 2} .0037416{col 37}{space 1}  -92.31{col 46}{space 3}0.000{col 54}{space 4} .2397839{col 67}{space 3} .2544723
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 2.123105{col 26}{space 2} .0481811{col 37}{space 1}   33.18{col 46}{space 3}0.000{col 54}{space 4} 2.030622{col 67}{space 3}   2.2198
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 2.439368{col 26}{space 2} .0602928{col 37}{space 1}   36.08{col 46}{space 3}0.000{col 54}{space 4} 2.323864{col 67}{space 3} 2.560612
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  1.99477{col 26}{space 2} .0546648{col 37}{space 1}   25.20{col 46}{space 3}0.000{col 54}{space 4} 1.890322{col 67}{space 3}  2.10499
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3650164{col 26}{space 2} .0093927{col 37}{space 1}  -39.17{col 46}{space 3}0.000{col 54}{space 4} .3470405{col 67}{space 3} .3839233
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.047418{col 26}{space 2} .0880607{col 37}{space 1}    0.55{col 46}{space 3}0.582{col 54}{space 4} .8880995{col 67}{space 3} 1.235318
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6533345{col 26}{space 2} .0293075{col 37}{space 1}   -9.49{col 46}{space 3}0.000{col 54}{space 4}  .598276{col 67}{space 3}   .71346
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4965364{col 26}{space 2} .0199871{col 37}{space 1}  -17.39{col 46}{space 3}0.000{col 54}{space 4} .4588201{col 67}{space 3} .5373531
{txt}{space 12} {c |}
{space 3}1.BornUSA {c |}{col 14}{res}{space 2} 1.616853{col 26}{space 2} .0559226{col 37}{space 1}   13.89{col 46}{space 3}0.000{col 54}{space 4} 1.510744{col 67}{space 3} 1.730415
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 2.285712{col 26}{space 2} .0496968{col 37}{space 1}   38.02{col 46}{space 3}0.000{col 54}{space 4} 2.190231{col 67}{space 3} 2.385356
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  3.09681{col 26}{space 2} .0454163{col 37}{space 1}   77.08{col 46}{space 3}0.000{col 54}{space 4} 3.008948{col 67}{space 3} 3.187237
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .9236658{col 26}{space 2} .0841729{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4} .7724014{col 67}{space 3} 1.104553
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 2.234007{col 26}{space 2} .0397061{col 37}{space 1}   45.22{col 46}{space 3}0.000{col 54}{space 4} 2.157426{col 67}{space 3} 2.313308
{txt}{space 12} {c |}
{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} 1.005814{col 26}{space 2} .0019232{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} 1.002046{col 67}{space 3} 1.009595
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2} .1610588{col 26}{space 2} .0819231{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4} .0593534{col 67}{space 3} .4370421
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2} .9999823{col 26}{space 2} 6.03e-06{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4} .9999705{col 67}{space 3} .9999942
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} 1.000019{col 26}{space 2} 9.50e-06{col 37}{space 1}    1.97{col 46}{space 3}0.049{col 54}{space 4}        1{col 67}{space 3} 1.000037
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 1.000001{col 26}{space 2} .0000116{col 37}{space 1}    0.09{col 46}{space 3}0.930{col 54}{space 4} .9999782{col 67}{space 3} 1.000024
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2} .9999616{col 26}{space 2} .0000414{col 37}{space 1}   -0.93{col 46}{space 3}0.354{col 54}{space 4} .9998803{col 67}{space 3} 1.000043
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} 1.000096{col 26}{space 2} .0000666{col 37}{space 1}    1.44{col 46}{space 3}0.149{col 54}{space 4} .9999655{col 67}{space 3} 1.000227
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2} .9998863{col 26}{space 2} .0000244{col 37}{space 1}   -4.66{col 46}{space 3}0.000{col 54}{space 4} .9998384{col 67}{space 3} .9999342
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0000985{col 26}{space 2} .0000107{col 37}{space 1}  -84.92{col 46}{space 3}0.000{col 54}{space 4} .0000796{col 67}{space 3}  .000122
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' == 7 | `model_version' == 8){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11763920

{txt}Expression: Pr(Suic), predict(pr)
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Change}{space 1}{space 1}{ralign 9:p-value}{space 1}{space 1}{ralign 9:LL}{space 1}{space 1}{ralign 9:UL}{space 1}{space 1}{ralign 9:z-value}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001842}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001888}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001797}}}{space 1}{space 1}{ralign 9:{res:{sf:-7.94e+01}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000857}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000810}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000903}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.63e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001098}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001046}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001150}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.11e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000759}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000701}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000817}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.58e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000241}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000194}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000288}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.00e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000098}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0014948}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000158}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000038}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.19e+00}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000339}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000395}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000283}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.19e+01}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001090}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001133}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001048}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.01e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000081}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5902509}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000215}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000378}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5386619}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000595}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000697}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000494}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.15e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000865}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000936}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000793}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.37e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001172}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000875}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001469}}}{space 1}{space 1}{ralign 9:{res:{sf:7.7447833}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000495}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000397}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000594}}}{space 1}{space 1}{ralign 9:{res:{sf:9.8678868}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000226}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000155}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000297}}}{space 1}{space 1}{ralign 9:{res:{sf:6.2723927}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000677}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000269}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000991}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000362}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.22e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000946}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001254}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000638}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.03e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000269}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000378}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000160}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.85e+00}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000581}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000512}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000650}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.66e+01}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001202}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001116}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001287}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.75e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001959}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001897}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002022}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.17e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000071}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3663394}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000226}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000084}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9037989}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001153}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001100}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001207}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.23e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000758}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000660}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000855}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.53e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001273}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001448}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001098}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.43e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000048}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3511377}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000150}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9328602}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0002031}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0002186}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001875}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.56e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000806}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000886}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000726}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.97e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001225}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001073}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001376}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.58e+01}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Std Err}{space 1}{space 1}{ralign 9:From}{space 1}{space 1}{ralign 9:To}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002447}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000605}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001620}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001861}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001522}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001620}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001861}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000031}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001620}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001522}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001861}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001522}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001717}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000627}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000151}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001717}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001799}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000052}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001717}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001122}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000037}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001717}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000853}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000151}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000627}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001799}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000627}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001122}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000627}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000853}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000160}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001799}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001122}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000157}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001799}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000853}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000056}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001122}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000853}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000943}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001524}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000044}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000935}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002136}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000935}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002894}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000079}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000935}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000864}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000935}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002088}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002136}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002894}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000089}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002136}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000864}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000052}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002136}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002088}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000079}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002894}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000864}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000041}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002894}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002088}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000077}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000864}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002088}}}{space 1}
{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9998526}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_7.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Aug 2020, 00:52:38
{txt}{.-}
{smcl}
{txt}{sf}{ul off}